---
title: Agent-to-Agent (A2A) Protocol
description: Enable CrewAI agents to delegate tasks to remote A2A-compliant agents for specialized handling
icon: network-wired
mode: "wide"
---

## A2A Agent Delegation

CrewAI supports the Agent-to-Agent (A2A) protocol, allowing agents to delegate tasks to remote specialized agents. The agent's LLM automatically decides whether to handle a task directly or delegate to an A2A agent based on the task requirements.

<Note>
  A2A delegation requires the `a2a-sdk` package. Install with: `uv add 'crewai[a2a]'` or `pip install 'crewai[a2a]'`
</Note>

## How It Works

When an agent is configured with A2A capabilities:

1. The LLM analyzes each task
2. It decides to either:
   - Handle the task directly using its own capabilities
   - Delegate to a remote A2A agent for specialized handling
3. If delegating, the agent communicates with the remote A2A agent through the protocol
4. Results are returned to the CrewAI workflow

## Basic Configuration

Configure an agent for A2A delegation by setting the `a2a` parameter:

```python Code
from crewai import Agent, Crew, Task
from crewai.a2a import A2AConfig

agent = Agent(
    role="Research Coordinator",
    goal="Coordinate research tasks efficiently",
    backstory="Expert at delegating to specialized research agents",
    llm="gpt-4o",
    a2a=A2AConfig(
        endpoint="https://example.com/.well-known/agent-card.json",
        timeout=120,
        max_turns=10
    )
)

task = Task(
    description="Research the latest developments in quantum computing",
    expected_output="A comprehensive research report",
    agent=agent
)

crew = Crew(agents=[agent], tasks=[task], verbose=True)
result = crew.kickoff()
```

## Configuration Options

The `A2AConfig` class accepts the following parameters:

<ParamField path="endpoint" type="str" required>
  The A2A agent endpoint URL (typically points to `.well-known/agent-card.json`)
</ParamField>

<ParamField path="auth" type="AuthScheme" default="None">
  Authentication scheme for the A2A agent. Supports Bearer tokens, OAuth2, API keys, and HTTP authentication.
</ParamField>

<ParamField path="timeout" type="int" default="120">
  Request timeout in seconds
</ParamField>

<ParamField path="max_turns" type="int" default="10">
  Maximum number of conversation turns with the A2A agent
</ParamField>

<ParamField path="response_model" type="type[BaseModel]" default="None">
  Optional Pydantic model for requesting structured output from an A2A agent. A2A protocol does not
  enforce this, so an A2A agent does not need to honor this request.
</ParamField>

<ParamField path="fail_fast" type="bool" default="True">
  Whether to raise an error immediately if agent connection fails. When `False`, the agent continues with available agents and informs the LLM about unavailable ones.
</ParamField>

<ParamField path="trust_remote_completion_status" type="bool" default="False">
  When `True`, returns the A2A agent's result directly when it signals completion. When `False`, allows the server agent to review the result and potentially continue the conversation.
</ParamField>

## Authentication

For A2A agents that require authentication, use one of the provided auth schemes:

<Tabs>
  <Tab title="Bearer Token">
    ```python Code
from crewai.a2a import A2AConfig
from crewai.a2a.auth import BearerTokenAuth

agent = Agent(
    role="Secure Coordinator",
    goal="Coordinate tasks with secured agents",
    backstory="Manages secure agent communications",
    llm="gpt-4o",
    a2a=A2AConfig(
        endpoint="https://secure-agent.example.com/.well-known/agent-card.json",
        auth=BearerTokenAuth(token="your-bearer-token"),
        timeout=120
    )
)
    ```
  </Tab>

  <Tab title="API Key">
    ```python Code
from crewai.a2a import A2AConfig
from crewai.a2a.auth import APIKeyAuth

agent = Agent(
    role="API Coordinator",
    goal="Coordinate with API-based agents",
    backstory="Manages API-authenticated communications",
    llm="gpt-4o",
    a2a=A2AConfig(
        endpoint="https://api-agent.example.com/.well-known/agent-card.json",
        auth=APIKeyAuth(
            api_key="your-api-key",
            location="header",  # or "query" or "cookie"
            name="X-API-Key"
        ),
        timeout=120
    )
)
    ```
  </Tab>

  <Tab title="OAuth2">
    ```python Code
from crewai.a2a import A2AConfig
from crewai.a2a.auth import OAuth2ClientCredentials

agent = Agent(
    role="OAuth Coordinator",
    goal="Coordinate with OAuth-secured agents",
    backstory="Manages OAuth-authenticated communications",
    llm="gpt-4o",
    a2a=A2AConfig(
        endpoint="https://oauth-agent.example.com/.well-known/agent-card.json",
        auth=OAuth2ClientCredentials(
            token_url="https://auth.example.com/oauth/token",
            client_id="your-client-id",
            client_secret="your-client-secret",
            scopes=["read", "write"]
        ),
        timeout=120
    )
)
    ```
  </Tab>

  <Tab title="HTTP Basic">
    ```python Code
from crewai.a2a import A2AConfig
from crewai.a2a.auth import HTTPBasicAuth

agent = Agent(
    role="Basic Auth Coordinator",
    goal="Coordinate with basic auth agents",
    backstory="Manages basic authentication communications",
    llm="gpt-4o",
    a2a=A2AConfig(
        endpoint="https://basic-agent.example.com/.well-known/agent-card.json",
        auth=HTTPBasicAuth(
            username="your-username",
            password="your-password"
        ),
        timeout=120
    )
)
    ```
  </Tab>
</Tabs>

## Multiple A2A Agents

Configure multiple A2A agents for delegation by passing a list:

```python Code
from crewai.a2a import A2AConfig
from crewai.a2a.auth import BearerTokenAuth

agent = Agent(
    role="Multi-Agent Coordinator",
    goal="Coordinate with multiple specialized agents",
    backstory="Expert at delegating to the right specialist",
    llm="gpt-4o",
    a2a=[
        A2AConfig(
            endpoint="https://research.example.com/.well-known/agent-card.json",
            timeout=120
        ),
        A2AConfig(
            endpoint="https://data.example.com/.well-known/agent-card.json",
            auth=BearerTokenAuth(token="data-token"),
            timeout=90
        )
    ]
)
```

The LLM will automatically choose which A2A agent to delegate to based on the task requirements.

## Error Handling

Control how agent connection failures are handled using the `fail_fast` parameter:

```python Code
from crewai.a2a import A2AConfig

# Fail immediately on connection errors (default)
agent = Agent(
    role="Research Coordinator",
    goal="Coordinate research tasks",
    backstory="Expert at delegation",
    llm="gpt-4o",
    a2a=A2AConfig(
        endpoint="https://research.example.com/.well-known/agent-card.json",
        fail_fast=True
    )
)

# Continue with available agents
agent = Agent(
    role="Multi-Agent Coordinator",
    goal="Coordinate with multiple agents",
    backstory="Expert at working with available resources",
    llm="gpt-4o",
    a2a=[
        A2AConfig(
            endpoint="https://primary.example.com/.well-known/agent-card.json",
            fail_fast=False
        ),
        A2AConfig(
            endpoint="https://backup.example.com/.well-known/agent-card.json",
            fail_fast=False
        )
    ]
)
```

When `fail_fast=False`:
- If some agents fail, the LLM is informed which agents are unavailable and can delegate to working agents
- If all agents fail, the LLM receives a notice about unavailable agents and handles the task directly
- Connection errors are captured and included in the context for better decision-making

## Best Practices

<CardGroup cols={2}>
  <Card title="Set Appropriate Timeouts" icon="clock">
    Configure timeouts based on expected A2A agent response times. Longer-running tasks may need higher timeout values.
  </Card>

  <Card title="Limit Conversation Turns" icon="comments">
    Use `max_turns` to prevent excessive back-and-forth. The agent will automatically conclude conversations before hitting the limit.
  </Card>

  <Card title="Use Resilient Error Handling" icon="shield-check">
    Set `fail_fast=False` for production environments with multiple agents to gracefully handle connection failures and maintain workflow continuity.
  </Card>

  <Card title="Secure Your Credentials" icon="lock">
    Store authentication tokens and credentials as environment variables, not in code.
  </Card>

  <Card title="Monitor Delegation Decisions" icon="eye">
    Use verbose mode to observe when the LLM chooses to delegate versus handle tasks directly.
  </Card>
</CardGroup>

## Supported Authentication Methods

- **Bearer Token** - Simple token-based authentication
- **OAuth2 Client Credentials** - OAuth2 flow for machine-to-machine communication
- **OAuth2 Authorization Code** - OAuth2 flow requiring user authorization
- **API Key** - Key-based authentication (header, query param, or cookie)
- **HTTP Basic** - Username/password authentication
- **HTTP Digest** - Digest authentication (requires `httpx-auth` package)

## Learn More

For more information about the A2A protocol and reference implementations:

- [A2A Protocol Documentation](https://a2a-protocol.org)
- [A2A Sample Implementations](https://github.com/a2aproject/a2a-samples)
- [A2A Python SDK](https://github.com/a2aproject/a2a-python)
